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Global Journal of Management and Business Research: B Economics and Commerce Volume 15 Issue 10 Version 1.0 Year 2015 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 2249-4588 & Print ISSN: 0975-5853 Exchange Rate Volatility on Investment and Growth in Nigeria, an Empirical Analysis By Adelowokan Oluwaseyi A., Adesoye A. B. & Balogun Oluwakemi D. Olabisi Onabanjo University, Nigeria Abstract- This paper examines the effect of exchange rate volatility on investment and growth in Nigeria over the period of 1986 to 2014. The vector error correction method, impulse responses function, co-integration and Augmented Dickey Fuller (ADF) test for stationarity were employed to capture the interactions between the variables. The results confirm the existence of long run relationship between exchange rate, investment, interest rate, inflation and growth. Finally the results show that exchange rate volatility has a negative effect with investment and growth while exchange rate volatility has a positive relationship with inflation and interest rate in Nigeria. Based on our findings, we recommended that the policy makers should developed sound exchange rate management system in the country potent enough for better growth in the economy. Keywords: exchange rate, volatility, investment, VAR. GJMBR - B Classification : JEL Code: P45 ExchangeRateVolatilityonInvestmentandGrowthinNigeriaanEmpiricalAnalysis Strictly as per the compliance and regulations of: © 2015. Adelowokan Oluwaseyi A., Adesoye A. B. & Balogun Oluwakemi D. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/bync/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Exchange Rate Volatility on Investment and Growth in Nigeria, An Empirical Analysis Keywords: exchange rate, volatility, investment, VAR. I I. Introduction n Nigeria, exchange rate management has undergone large changes over four decades. In 1960s Nigeria operated a fixed exchange regime which was fixed at par with the British pound and later the American dollar in addition to restrictions on import via strict administrative controls on foreign exchange. In 1978, the monetary authorities pegged the naira to a basket of 12 currencies of her major trading partners. The sharp fall in international oil price and consequent decline in foreign exchange receipts in the early 1980s were such that the economy could not meet its international financial commitments, and to migrate the challenges, the stabilization act of 1982 was implemented which led to accelerated depreciation of the naira. In Nigeria, the management of the exchange rate is vested in the Central bank of Nigeria (CBN) and since the introduction of the structural Adjustment Programme (SAP) in 1986; exchange rate management has been a core macroeconomic policy function. Mordi, (2006) agreed that exchange rate has appreciated and has been relatively stable. Benson and Victor, (2012) and Aliyu, (2011) noted that despite various efforts by the government to maintain a stable exchange rate, the naira has depreciated throughout the 80’s to date. Exchange rate volatility became significant following the breakdown of the Bretton Wood Author α : Lecturer, Department of Economics, O.O.U , Ago- Iwoye. e-mail: [email protected], Author σ : (P.hd.) Lecturer, Department of Economics, O.O.U , AgoIwoye. Author ρ : M.sc student Economics Department, O.O.U , Ago- Iwoye, Ogun State, Nigeria. Agreement in 1973 after which exchange rate became flexible among world currencies. Literature put it that exchange rate became more volatile in Nigeria after the introduction of widely known currency control measures called the Structural Adjustment Programme (SAP) in 1986. Volatility in Nigeria manifests in different forms ranging from volatility in real growth rates, price inflation, investment per capita and government revenues per capita to fluctuations in terms of trade and real exchange rate. There are numerous reasons why research into the effect of exchange rate volatility on investment inflows is important for a developing resource-based economy like Nigeria. First, macroeconomic volatility represents a measure of the uncertainty that economic agents face about the future. In turn, uncertainty affects the future level of growth and investment. Second, government policy is often directed towards reducing volatility by smoothing out the fluctuations in the time path of income, price and investment, among others. According to the literature, exchange rate volatility has to do with the unusual movements of the exchange rate. Exchange rate is one of the economic indicators which directly affect investment as such as its role in the overall economic objectives of a country cannot be underestimated. This gives confidence to why the public sectors, foreign investor and private individual pay a lot of attention to the exchange rate volatility. Since September 1986, when the market determined exchange rate system was introduced via the second tier foreign exchange market, the naira exchange rate has exhibited the features of continuous depreciation and instability. People have not been investing due to exchange rate volatility. This instability and continued depreciation of the naira in the foreign exchange market has resulted in declines in the investment, standard of living of the populace, increased cost of production which also leads to cost push inflation. It has also tended to undermine the international competitiveness of non-oil exports and make planning and projections difficult at both micro and macro levels of the economy. A good number of small and medium scale enterprises have been strangled out as a result of low dollar/ naira exchange rate and so many other problems resulting from fluctuations in exchange rates can also be identified. The purpose of this paper is therefore, to examine the effect of exchange rate volatility on © 20 15 Global Journals Inc. (US) Year volatility on investment and growth in Nigeria over the period of 1986 to 2014. The vector error correction method, impulse responses function, co-integration and Augmented Dickey Fuller (ADF) test for stationarity were employed to capture the interactions between the variables. The results confirm the existence of long run relationship between exchange rate, investment, interest rate, inflation and growth. Finally the results show that exchange rate volatility has a negative effect with investment and growth while exchange rate volatility has a positive relationship with inflation and interest rate in Nigeria. Based on our findings, we recommended that the policy makers should developed sound exchange rate management system in the country potent enough for better growth in the economy. 21 Global Journal of Management and Business Research ( B ) Volume XV Issue X Version I Abstract- This paper examines the effect of exchange rate 2015 Adelowokan Oluwaseyi A. α, Adesoye A. B. σ & Balogun Oluwakemi D. ρ Exchange Rate Volatility on Investment and Growth in Nigeria, an Empirical Analysis investment and growth in Nigeria. The vector error correction method is applied to estimate the impulse response functions for investment and growth in order to determine how investment and growth responds to exchange rate volatility. Year 2015 II. Global Journal of Management and Business Research ( B ) Volume XV Issue X Version I 22 Literature Review Several studies have been conducted on the effect of exchange rate volatility. Few of the studies have conducted both exchange rate volatility on growth and investment in Nigeria. Manalo, Perera and Rees (2014) examine the effects of exchange rate movements on the Australian economy using the structural vector auto-regression model using seasonally adjusted data at quarterly frequencies for the period of 1985Q1 to 2013Q2. They found out that a temporary 10 per cent appreciation of the real exchange rate that is unrelated to the terms of trade or interest rate differentials lowers the level of real GDP over the subsequent one-to-two years by 0.3 per cent and year-ended inflation by 0.3 percentage points. Chowdhry and Wheeler (2008) in an empirical analysis studied the relationship between volatility of exchange rate for the four developed countries of Canada, Japan, United State and United Kingdom. Using a number of variables this study applied vector auto regressive (VAR) approach and found that shocks to exchange rate volatility have positive and significant impact on flow of FDI. Akeju(2014) also examines the impact of real exchange rate on terms of trade and ecopnomic growth which relies on cointegration techniquies and error correction model using annual data covering from 19802012. It was revealed that a real exchange rate moves along the same direction with terms of trade in the long run. Rasaq (2013) examined the impact of exchange rate volatility on the macro economic variables in nigeria and findings shows that exchange rate volatilty has a positive influence on GDP, FDI and trade openess with a negative influence on the inflationary rate in the country. Dada and oyeranti (2012) examines exchange rate and macroeconomic aggregates in Nigeria. The result shows that there is no evidence of a strong direction between changes in the exchange rate and GDP growth. Rather, the countrys growth has been directly affected by fiscal and monetary policies and other economic variables particularly the growth of exports which is marjorly oil. In short, the nature of the effect of exchange rate volatility on investment and growth is yet unresolved. There is therefore the need for more empirical research on the subject matter. This is particularly important in view of the nature of exchange rate in developing countries like Nigeria. III. Theoretical Underpinnings Romer in his first paper on endogenous growth in 1986 presented a variant on Arrow’s model which is © 2015 1 Global Journals Inc. (US) known as learning by investment. He assumes creation of knowledge as a side product of investment. He takes knowledge as an input in the production function of the following form Y = A(R) F (Ri, Ki ,Li) Where Y = aggregate output/Gross Domestic Product (GDP), A = public stock of knowledge R and Ri = stock of expenditure i, Ki and Li = capital stock and labour stock of firm i respectively. He assume the function F homogeneous of degree one in all its input Ri, Ki, and Li and treat Ri as a rival good. Romer took three key elements in his model, namely externalities, increasing returns in the production of output and diminishing returns in the production of new knowledge. According to Romer, it is spill-over’s from research efforts by a firm that leads to the creation of new knowledge by other firms. In other words, words, new research technology by a firm spills-over instantly across the entire economy. In his model, new knowledge is the ultimate determinant of long-run growth which is determined by investment in research technology. Research technology exhibits diminishing returns which mean that investment in research technology will not double knowledge. Moreover, the firm investing in research technology will not be the exclusive beneficiary of the increase in knowledge. The other firms also make use of the new knowledge due to the inadequacy of patent protection and increase their production. Thus the production of goods from increased knowledge displays increasing returns and competitive equilibrium is consistent with increasing aggregate returns owing to externalities. Thus Romer takes investment in research technology as endogenous factor in terms of the acquisition of new knowledge by rational profit. IV. Methodology The goal of the paper is to ascertain if exchange rate volatility enhance investment and economic growth. This study will adopt Vector Autoregressive (VAR model). The vector autoregressive (VAR) model is one of the most successful, flexible, and easy to use models for the analysis of multivariate time series. It is a natural extension of the univariate autoregressive model to dynamic multivariate time series. This study will adapt the model specified by (Sims 1980). He said a pathorder VAR is also called a VAR with p lags. The process of choosing the maximum lag p in the VAR model requires special attention because inference is dependent on correctness of the selected lag order: A p-th order VAR, denoted VAR (p), is Exchange Rate Volatility on Investment and Growth in Nigeria, an Empirical Analysis -------------- (i) The model represented as: for this EXR = c + A1Gdpt-1 + A2Investt-2 + A3Inft-3 + A4Intt-4 + et study is therefore --------------------- (2) p p p p p i =1 i =1 i =1 i =1 i =1 p p p p p i =1 i =1 i =1 i =1 i =1 Year 2015 INF = Inflation Rate INT = Interest Rate Et = Error Term The VAR model is expressed in a system as: Where: EXR = Exchange rate GDP = Gross Domestic Product INVEST = Investment EXRt = c1 + ∑ π 11,i EXRt −i + ∑ π 12i GDPt −i + ∑ π 13,i INVESTt −i + ∑ π 14,i INFt −i + ∑ π 15,i INTt −i + µ 1,t (3) GDPt = c1 + ∑ π 11,i GDPt −i + ∑ π 12i EXRt −i + ∑ π 13,i INVESTt −i + ∑ π 14,i INFt −i + ∑ π 15,i INTt −i + µ 1,t (4) p p p p p i =1 i =1 i =1 i =1 i =1 INVESTt = c1 + ∑ π 11,i INVESTt −i + ∑ π 12i EXRt −i + ∑ π 13,i GDPt −i + ∑ π 14,i INFt −i + ∑ π 15,i INTt −i + µ 1,t p p p p p i =1 i =1 i =1 i =1 i =1 (5) INFt = c1 + ∑ π 11,i INFt −i + ∑ π 12i EXRt −i + ∑ π 13,i INVESTt −i + ∑ π 14,i GDPt −i + ∑ π 15,i INTt −i + µ 1,t p p p p p i =1 i =1 i =1 i =1 i =1 (6) INTt = c1 + ∑ π 11,i INTt −i + ∑ π 12i EXRt −i + ∑ π 13,i INVESTt −i + ∑ π 14,i INFt −i + ∑ π 15,i GDPt −i + µ 1,t (7) The VAR (p) system equation (3) to equation (7) can be represented in a reduced form within a matrix framework as: EXRt GDPt INVESTt INFt INTt c1 c2 = c3 + c4 c 5 π 11 π 21 p π 31 ∑ i =1 π 41 π 51 π 12 π 13 π 22 π 23 π 32 π 33 π 42 π 52 π 43 π 53 Exchange Rate Volatility is measured by taking the standard deviation of the moving average of the logarithm of real exchange rate, as well as a dummy capturing the amount of times the exchange rate moves above and below the average values of the real effective exchange rate in predetermined intervals. π 14 π 24 π 34 × π 44 π 54 µ1,t EXRt − i µ 2 ,t GDPt − i INVESTt − i + µ 3,t µ 4 ,t INFt − i INTt − i µ 5 ,t (8) Nigeria from 1986 to 2014. This enables to determine causal relationship among exchange rate volatility, investment and growth proxy as growth rate of gross domestic product (GDP). V. Empirical Result and Discussions a) Trend Analysis Result This section of this study access the trend of exchange rate volatility on investment and growth in © 20 15 Global Journals Inc. (US) 23 Global Journal of Management and Business Research ( B ) Volume XV Issue X Version I where the l-periods back observation yt−l is called the lthlag of y, c is a k × 1 vector of constants (intercepts), Ai is a time-invariant k × kmatrix and et is a k × 1 vector of error terms satisfying. 2013 2010 2007 2004 2001 1998 1995 GDP Year Figure 2 : Gross Domestic Product (US$'billion) 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 2014 2012 2010 2006 2004 2002 2000 1998 1996 1994 1992 1990 1986 INVEST 2008 1992 US$'billion 24 Global Journal of Management and Business Research ( B ) Volume XV Issue X Version I year 45000 40000 35000 30000 25000 20000 15000 10000 5000 0 Figure 1 : Exchange rate Volatility (US$'billion) 1988 2015 Year 1989 US$'billion 350 300 250 200 150 100 50 0 1986 US$,billion Exchange Rate Volatility on Investment and Growth in Nigeria, an Empirical Analysis year Figure 3 : Investment (US$'billion) b) Descriptive Statistics Results Table 1 below presents the results of the time series properties of the variables included in the model. The descriptive statistics was carried out between exchange rate volatility, investment and growth in Nigeria (1986-2014). Table 1 EXR GDP INFR INT INVEST Mean 33.34287 12636.84 21.23017 12.60615 3965.474 Median 7.461668 6713.575 12.16854 12.59 3408.54 Maximum 291.8318 42396.77 76.75887 23.99 8439.51 Minimum 0.11754 134.6033 0.223606 4.704871 1916.04 Std. Dev. 68.35224 14319.1 19.95911 5.339686 2035.76 Skewness 3.091287 1.008109 1.490246 0.57736 1.078553 Kurtosis 11.3787 2.583133 3.935269 2.512348 2.925836 Jarque-Bera 131.0157 5.12202 11.791 1.898511 5.629148 Probability 0 0.077227 0.002752 0.387029 0.05993 Sum 966.9433 366468.3 615.6749 365.5784 114998.7 Sum Sq. Dev. 130816.8 5.74E+09 11154.25 798.343 1.16E+08 Observations 29 29 29 29 29 Source: Author’s computation, 2015. © 2015 1 Global Journals Inc. (US) Exchange Rate Volatility on Investment and Growth in Nigeria, an Empirical Analysis c) Unit Root Test Results This pre-test was carried out before estimating the long-run relationship between exchange rate volatility, investment and growth in Nigeria (1986-2014). Critical Level of Order of Statistics Values Significance Integration EXR -8.4651 -4.3393 1% I (1) GDP -4.6099 -4.3393 1% I (1) INFR -4.4641 -4.3943 1% I (1) 25 INT -4.52553 INVEST -6.9921 -4.3561 1% -4.3393 I (1) 1% I (1) Source: Author’s Computation, 2015. The Augmented Dickey Fuller (ADF) unit-root test results presented in table 2 indicate that exchange rate (EXR), gross domestic product (GDP), inflation (INFR), interest rate (INT) and investment (INVEST) are stationary at first difference. We then applied the Johansen-Juselius (1990) co-integration technique to determine whether there is at least one linear combination of these variables that is I(0). d) Co-integration Johansen (1990) approach is use to find out the existence or inexistence of a long-run relationship among the variables employed for this study in other to avoid biased results. The Johansen co-integration test for (EXR), (GDP), (INFR), (INT) and (INVEST) are presented in the table below. Hp: rank = p (no deterministic trend in the data) Hr: rank r < p (co-integration relations) Series: EXR GDP INFR INT INVEST Hypothesized No. of CE(s) None At most 1 At most 2 At most 3 At most 4 Eigenvalue Trace Statistics Likelihood Ratio 0.795271 0.760883 0.348426 0.254026 0.003566 97.28870* 56.05092* 18.85003 7.712555 0.092880 Max-Eigen Statistics 5% Sig. lev. Likelihood Ratio 69.81889 47.85613 29.79707 15.49471 3.841466 41.23778* 37.20089* 11.13748 7.619675 0.092880 0.05 Crit. Val. 33.87687 27.58434 21.13162 14.26460 0.7605 * denotes rejection of the hypothesis at 5% significance level. Likelihood ratio test of both Trace and Max-Eigen indicates 2 co-integrating equation(s) Source: Author’s computation (2015). The above co-integration result tests for long run relationship between the dependent variable and the independent variables (EXR), (GDP), (INFR), (INT) and (INVEST). For rank (0), since the trace statistics (0.795271) is more than 5% critical value (69.81889), we reject the null hypothesis (there is no co-integration among variables). Otherwise, accept the alternate hypothesis indicating that there is a long run relationship among the variables. © 20 15 Global Journals Inc. (US) Global Journal of Management and Business Research ( B ) Volume XV Issue X Version I Variables 2015 ADF Year Table 2 : ADF Unit Root Test Results (Trend and Intercept) Exchange Rate Volatility on Investment and Growth in Nigeria, an Empirical Analysis e) Granger Causality Test Having established that there is a long-run relationship among the variables, the objective of this section is to determine the direction of causality between the dependent variable (EXR) and the independent variables (GDP, INF, INT, INVEST) in Nigeria for the period of 1986 to 2014. The Pair-wise Granger Causality test result is presented in the Table 5 below. Null Hypothesis Lag F-Statistic Probability GDP does not Granger Cause EXR 2 1.29562 0.2938 Accept EXR does not Granger Cause GDP 2 INFR does not Granger Cause EXR 0.6626 0.9190 2015 EXR does not Granger Cause INFR 2 2 0.41943 0.08482 Accept 2.23632 0.1306 Year Table 5 : Pair-wise Granger Causality Test Result INT does not Granger Cause EXR 2 0.02513 0.9752 Accept EXR does not Granger Cause INT 2 0.17139 0.8436 Accept INVEST does not Granger Cause EXR 2 0.19013 0.8282 Accept EXR does not Granger Cause INVEST 2 0.52496 0.5988 Accept INFR does not Granger Cause GDP 2 0.07808 0.5988 Accept GDP does not Granger Cause INFR 2 1.72511 0.2014 Accept INT does not Granger Cause GDP GDP does not Granger Cause INT 2 0.03623 1.71727 0.9645 0.2028 Accept Global Journal of Management and Business Research ( B ) Volume XV Issue X Version I 26 Remarks Accept Accept INVEST does not Granger Cause GDP 2 2 6.81810 0.0050 Accept Reject GDP does not Granger Cause INVEST 2 1.29693 0.2935 Accept INT does not Granger Cause INFR 2 6.71784 0.0053 Reject INFR does not Granger Cause INT 2 2.71481 0.0884 Reject INVEST does not Granger Cause INFR 2 1.23826 0.3093 Accept INFR does not Granger Cause INVEST 2 0.01137 0.9887 Accept INVEST does not Granger Cause INT 2 1.55009 0.2345 Accept INT does not Granger Cause INVEST 2 0.56282 0.5776 Accept Source: Author’s computation, 2015. The table above shows the causal relationship between exchange rate, investment, interest rate, inflation and growth in Nigeria between 1986 to 2014. The table revealed that GDP and EXR, INFR and EXR, INT and EXR, INVEST and EXR, INF and GDP, INT and GDP, INVEST and INF, INVEST and INT has no causality at 5% significance level. There is unidirectional causality between INVEST and GDP While INT and INF has bi-directional relationship at Lag 2 and 5% or significance level. f) Vector Error Correction Estimates Result The formulated and estimated vector error correction model (VECM) using an optimal lag structure of two is shown below to examine the dynamic effects of exchange rate volatility on investment and growth in Nigeria from 1986 to 2014. Table 6 : Estimated VECM Results for the Analysis of Exchange rate volatility on investment and growth Endogenous variable: EXR _GDP _INFR _INT _INVEST Econometric Method: VECM Estimate Sample: 1986-2014 Equation ECM D(EXR) -1.383746 (0.54922) [-2.51946] D(GDP) -5.879700 (17.1828) [-0.34219] D(INFR) 0.290144 (0.10537) [ 2.75369] D(INT) -0.000359 (0.03021) [-0.01188] D(INVEST) -23.24526 (11.7367) [-1.98057] D(EXR(-1)) 0.322968 (0.43047) [ 0.75027] 8.796626 (13.4675) [ 0.65317] -0.160456 (0.08258) [-1.94296] -0.005085 (0.02368) [-0.21472] 13.17914 (9.19896) [ 1.43268] © 2015 1 Global Journals Inc. (US) 3.262722 (8.44135) [ 0.38652] -0.063176 (0.05176) [-1.22050] -0.020610 (0.01484) [-1.38856] 7.604156 (5.76584) [ 1.31883] D(GDP(-1)) -0.002614 (0.00752) [-0.34757] 0.257383 (0.23525) [ 1.09409] -1.94E-06 (0.00144) [-0.00135] -2.31E-05 (0.00041) [-0.05573] 0.102106 (0.16069) [ 0.63544] D(GDP(-2)) -0.001446 (0.00735) [-0.19658] 0.386496 (0.23010) [ 1.67966] 0.001041 (0.00141) [ 0.73801] 3.98E-06 (0.00040) [ 0.00984] -0.404125 (0.15717) [-2.57124] D(INFR(-1)) 0.380760 (0.76110) [ 0.50027] -9.943477 (23.8116) [-0.41759] 0.126762 (0.14601) [ 0.86815] 0.072811 (0.04187) [ 1.73901] -10.84402 (16.2645) [-0.66673] D(INFR(-2)) -0.865093 (0.78024) [-1.10875] 25.63898 (24.4103) [ 1.05033] -0.521405 (0.14969) [-3.48335] -0.047959 (0.04292) [-1.11736] 13.89294 (16.6734) [ 0.83324] D(INT(-1)) 8.389574 (4.98301) [ 1.68364] -41.44684 (155.896) [-0.26586] 0.369651 (0.95596) [ 0.38668] 0.048553 (0.27412) [ 0.17712] 60.28723 (106.485) [ 0.56616] D(INT(-2)) -1.732057 (4.44637) [-0.38954] 178.1472 (139.107) [ 1.28065] -0.132236 (0.85301) [-0.15502] -0.313833 (0.24460) [-1.28305] 177.1733 (95.0168) [ 1.86465] D(INVEST(-1)) -0.006871 (0.00982) [-0.69976] -0.170825 (0.30718) [-0.55610] 0.004320 (0.00188) [ 2.29366] 0.000164 (0.00054) [ 0.30365] -0.602886 (0.20982) [-2.87335] D(INVEST(-2)) -0.012772 (0.01047) [-1.22021] 0.523666 (0.32746) [ 1.59916] 0.002665 (0.00201) [ 1.32726] -0.000312 (0.00058) [-0.54207] -0.123608 (0.22367) [-0.55263] C 12.15895 (18.4536) [ 0.65889] 621.3444 (577.331) [ 1.07624] -4.560084 (3.54022) [-1.28808] -0.477976 (1.01515) [-0.47084] 673.0130 (394.345) [ 1.70666] 0.472427 0.057905 1.139692 -226.3082 18.33140 0.661631 0.395769 2.488629 -93.85828 8.142944 0.509225 0.123616 1.320573 -61.38034 5.644641 0.611142 0.305610 2.000257 -216.3972 17.56901 0.632117 R-squared Adj. R-squared 0.343065 2.186868 F-statistic Log likelihood -136.7861 11.44508 Akaike AIC : Source: Authors’ computation (2015). It has been pointed out in the literature that individual coefficients from the error-correction model are hard to interpret in the case of vector-autoregressive model. Consequently, the dynamic properties of the model are analyzed by examining the impulse response functions and the variance decompositions. g) Impulse Responses Analysis The impulse response result allow us to see the shock from the impulse sector which is the exchange rate in this study case and the response sector include investment, and gross domestic product. © 20 15 Global Journals Inc. (US) Year 0.257668 (0.26982) [ 0.95498] 27 Global Journal of Management and Business Research ( B ) Volume XV Issue X Version I D(EXR(-2)) 2015 Exchange Rate Volatility on Investment and Growth in Nigeria, an Empirical Analysis Exchange Rate Volatility on Investment and Growth in Nigeria, an Empirical Analysis Response to Cholesky One S.D. Innovations Res pons e of EXR to EXR Res pons e of EXR to GDP 80 80 60 60 40 40 20 20 0 0 -20 -20 -40 -40 1 2 3 4 5 6 7 8 9 1 10 2 3 Year 2015 Res pons e of EXR to INFR Global Journal of Management and Business Research ( B ) Volume XV Issue X Version I 28 4 5 6 7 8 9 10 8 9 10 Res pons e of EXR to INT 80 80 60 60 40 40 20 20 0 0 -20 -20 -40 -40 1 2 3 4 5 6 7 8 9 10 9 10 1 2 3 4 5 6 7 Res pons e of EXR to INVEST 80 60 40 20 0 -20 -40 1 2 3 4 5 6 7 8 Impulse Response plot of exchange rate movement on investment and growth shocks. Figure I below presents the contemporaneous response of exchange rate to Cholesky one squares variances shocks on investment and growth performance. As shocks in exchange rate (EXR) arise, the response of gross domestic product (GDP) was negative .This is similar to the response of exchange rate (EXR) to investment (INVEST). Contrary, gross domestic product (GDP) and investment (INVEST) react negatively. h) Variance Decomposition This section presents the variance decomposition, which seperates the variation in an endogenous variable into the component shocks of the VEC model. The table7 below present the variance decomposition of exchange rate to innovation shocks from investment, interest rate, inflation and growth. In the second column, the labelled “S.E.” contains the forecast error of the variable at a given forecast horizon. The source of this forecast error is the variation in the current and future values of the innovations to each endogenous variable in the VECM.. The other columns for each of variables give the percentage of the forecast variance due to each innovation, with each row adding up to 100. Table 7 : Variance Decomposition Analysis of Exchange rate volatility on Investment and Growth Period S.E. 1 2 3 4 5 6 7 8 9 10 63.53453 68.11116 74.33258 78.78545 86.02979 91.13239 93.99140 97.84740 102.0088 106.0786 EXR 100.0000 87.01636 74.75622 66.56027 57.58950 51.38730 48.84268 45.31313 41.90845 39.00045 source: Author’s computation,2015. © 2015 1 Global Journals Inc. (US) GDP 0.000000 1.439582 1.292649 1.168008 1.240587 2.520283 2.604488 4.354474 5.637120 6.351065 INFR 0.000000 8.704769 7.558204 7.070064 8.290699 12.01408 12.71083 12.15743 12.49281 12.74532 INT 0.000000 2.833542 16.36513 24.79396 32.50966 33.47854 34.99580 37.39347 39.20906 41.19321 INVEST 0.000000 0.005744 0.027795 0.407697 0.369554 0.599798 0.846201 0.781505 0.752558 0.709953 Exchange Rate Volatility on Investment and Growth in Nigeria, an Empirical Analysis The table above present the variation in EXR, GDP, INFR, INT and INVEST due to the shocks in decomposed into related policy instruments. The results of the percentage of exchange rate volatility accounted by the considered policy instrument shocks are presented in Table 7.1 Table 7.1 : Percentage of exchange rate, GDP, INFR, INT and INVEST Variation due to Shocks Overall % share of EXR, GDP, INFR, INT and INVEST shocks Exchange rate shocks 61.24% Growth shocks 2.66% Inflation shocks 9.37% Interest rate shocks 26.28% Investment shocks 0.45% VI. Conclusion and Recommendations This paper examines the relationship between exchange rate, its volatility on investment and growth both theoretically and empirically from 1986 to 2014 in Nigeria. Exchange rate has poorly been managed over time and the time is long overdue to salvage the situation from getting worse. The theoretical issue on exchange rate was discussed and empirical finding were done to know the past findings on authors work that have done research relating to exchange rate volatility. The model adopted for this research work is vector autoregressive model (VAR).The Augmented Dickey Fuller (ADF) test was carried out to test for unit roots for the variables involved. Descriptive statistics was used to understand the data; trend analysis was used to know the trend and pattern of exchange rate volatility on investment and growth. Johansen cointegration test was used to determine whether there is long-run relationship among the variables and the results reveal the presence of two co-integration equations which indicate the existence of long run relationship among the five variables. Granger causality was used to know the causal effect among the variables, impulse response econometric estimators was used to known the impulse responses among the variables, the vector error correction method (VECM) was used to known whether there is any effect and the variance decomposition was also used to know the percentage of shocks in the variable . Conclusively the volatility in exchange rate has a negative influence on investment and gross domestic product (GDP) which proxed growth and exchange rate volatility has significant influence with inflation and interest rate. The empirical findings are in conformity with Diallo (2009) and Bleaney & Greenaway (2010) results findings. References Références Referencias 1. Akpan E.O and Atan J.A (2012). “ Effects of Exchange Rate Movements on Economic Growth in Nigeria”. Journal of Applied Statistics Vol. 2 No.2 2. Anifowose, O.K (1983) “The Relevance of Exchange Control in Nigeria balance of payments adjustments process’’ CBN. 3. Akeju kemi ( 2014), Real Exchange Rates , Terms of Trade and Economic Growth in Nigeria ( 19802012), Journal of Economics Theory 8 (2) : 19-23, 2014, ISSN: 1994-8212 4. Aliyu, S.R.U. (2011). “Impact of Oil Price Shock and Exchange Rate Volatility on Economic Growth in Nigeria” An Empirical Investigation, Research Journal of International Studies. 5. Axel G., Inessa L. and Alexei G. (2014), The Dynamics of Exchange Rate Volatility: A panel VAR approach. The dynamics of exchange rate volatility: A panel VAR approach. Journal of International Financial Markets, Institutions & Money 33 (2014) 1– 27. 6. Benson, U.O and Victor, E.O (2012). “Real Exchange Rate and Macroeconomic Performance: Testing for the Balassa-Samuelson Hypothesis in Nigeria. International Journal of Economics and Finance: 4 (2), 127-134. 7. Chaudhary G.M, Shah S.A. and Bagram M.M. (2012) Do Exchange Rate Volatility Effects Foreign Direct Investment? Evidence from Selected Asian Economies. Journal of Basic and Applied Scientific Research 2(4)3670-3681, 2012 ISSN 2090-4304. © 20 15 Global Journals Inc. (US) Year The general findings in this study have necessitated some policy directions which may be useful recommendations for policy authorities. Since the role of exchange rate volatility in investment indicates slight negative effect, it is appropriate for the authorities to develop sound exchange rate management in the country. The Central Bank should use the allocations and disbursement of foreign currencies as well as the naira to regulate the vacillations in exchange rate over time. Proper effective management of economic and noneconomic factors that will triggers exchange rate volatility. 29 Global Journal of Management and Business Research ( B ) Volume XV Issue X Version I The table revealed that shocks within itself (i.e exchange rate shocks), growth shocks, inflation shocks, interest rate shocks and investment shocks accounted for 61.24%, 2.66%, 9.37%, 26.28% and 0.45% of the total variation in exchange rate volatility in Nigeria respectively. It indicates that Investment is the least among various variable in Nigeria between 1986 to 2014. 2015 Source : Authors’ computation (2015). Year 2015 Exchange Rate Volatility on Investment and Growth in Nigeria, an Empirical Analysis Global Journal of Management and Business Research ( B ) Volume XV Issue X Version I 30 8. Chowdhry A, Wheeler M (2008). Does Real Exchange Rate Volatility Affect Foreign Direct Investment? Evidence from Four Developed Economies. Int. Trade J., 22(02). 9. Dada Eme A. and Oyeranti O. A. ( 2012), “Exchange Rate and Macro Economic Aggregates in Nigeria”, Journal of Economics and Sustainable Development, 3(2) 10. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. The American Statistical Association, 74, 427-431. 11. Jhingan, M.L (2006): International Economics. Vrinda Publications (p) LTD. B.5, Ashish Complex Copp. Ahlcon Public School, MayurVihar, Phase-1, Delhi-110091. 12. Johansen, S., & Juselius, K. (1990). 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Olanipekun, D.B (2013), Exchange rate volatility and economic activities in Nigeria. A Post-Field Report Submitted in Partial Fulfilment for the Award of a Doctoral Degree (Economics) of the University of Ibadan, Nigeria. CPPB6 18. Rasaq Akonji Danmola (2013), “The Impact of Exchange Rate Volatility on the Macroeconomic Variables in Nigeria”, European Scientific Journal 9 (7) 19. Renani H.S. and Mirfatah M. (2012), “The Impact of Exchange Rate Volatility on Foreign Direct Investment in Iran”. Journal of Procedia Economics and Finance 1 ( 2012 ) 365 – 373. © 2015 1 Global Journals Inc. (US)